Abstract: Abstract: Water chlorophyll-a is one of the most important indices for water quality monitoring. Remote sensing technology has strong advantages in monitoring both water and vegetation chlorophyll-a concentrations. Most of the current study on water chlorophyll-a monitoring chose the sensitive band based on the water chlorophyll-a spectral characteristics, and then established the inversion model. Some researchers established the water parameters inversion model based on an analytical physical mechanism, which are more complex and difficult to use in practice. And we also noticed that a vertical comparative analysis was needed for all these different inversion methods in the same area, and a few researchers used the water chlorophyll-a absorption similarity with leaf to build the water chlorophyll-a retrieval model. In this paper, a new water chlorophyll-a retrival index WCI (Water Chlorophyll-a Index) was built from the land surface vegetation chlorophyll retrieval index MTCI (MERIS terrestrial chlorophyll index), based on the in-situ water hyperspectral data and water chlorophyll-a content results in the laboratory in July 2012 in the Lijiang River, Guangxi Zhuang Autonomous Region. The MTCI was based on the fast climbing vegetation reflectance in 680-750 nm also called the "red edge." The MTCI was easy to calculate, and had a strong correlation with leaf chlorophyll-a content. From the beginning of 2004, the MTCI has became the core algorithm of the land chlorophyll-a product on ESA Envisat satellite's MERIS sensor. This index is now widely used in land leaf chlorophyll-a retrival and net primary productivity (NPP) estimation. The WCI index also uses the different ratio of characteristic bands to represent the water chlorophyll-a content. The WCI index uses hyperspectral water reflectance at 650, 685, and 696 nm. We used the traditional method at the same location to verify all these models's effect. The traditional methods consist of the reflectance model, reflectance ratio model, and the semi-analytical model (three bands model). The three traditional methods directly selected the water spectral reflectance at certain bands. Spectral smoothing can reduce the band noise at certain extent, but is easy to select the wrong band for the measured hyperspectral data because the absorption and reflection peak of water spectrum has big differences in different water spectrum curves. Our research also moticed that the change information of water spectrum was more useful compared with the water spectrum itself. Our results indicated that the new WCI index showed the best coefficient of determination 0.58 and the least RMSE 0.24 compared with the reflectance model, reflectance ratio model, and semi-analytical model. The test results also showed that the WCI model can retrieve the water chlorophyll-a content effectively at Tianjin City Haihe River. This method extended the idea of water chlorophyll-a content modeling from the view of the terrestrial vegetation chlorophyll-a monitoring, and has certain instructive effect on water chlorophyll-a content monitoring. More situ data of different water bodies is needed to verify the new model's robustness and effectiveness.